3 Methodology

“Research is defining the invisible.” —Steven Magee (author and researcher)

In the previous chapter, I described the two research areas this thesis seeks to explore: how people think about data and what they want from it; and the role data takes and should take in people’s relationships with organisations. In this chapter, I will explain my approach to conducting research in this area, detail the types of methods used, and explain how the different research activities I carried out contribute to the research objectives.

3.1 Forming a Research Paradigm: Ontology & Epistemology

good

To develop a research paradigm, one must reflect on two things (Guba, 1990):

• ontology: your outlook on the nature of reality, and
• epistemology: your beliefs on how knowledge of that reality is formed.

It will already be evident to the reader that individual human experience is at the forefront of my thinking. I believe that everyone experiences their own reality, informed by their own concepts and mental models of the world. This ontological stance, known as constructivism (Guba, 1990) argues new knowledge is formed by developing one’s own mental models in order to explain new experiences, as distinct from the positivist view that there is a single universal reality one can uncover. In today’s rapidly evolving technological landscape, people have no choice but to develop new mental models to make sense of new concepts such as posts, feeds, link sharing, syncing and blocking, in order that we might find value in them.

This idea that reality is constantly renegotiated by the individual is known as pragmatism (Campbell, 2011). People’s developing conceptions of reality are not purely an intellectual endeavour though; As Deweyan pragmatism states, our knowledge and thinking are tested by actions, not just reason, and that this is how we learn. Communication and interaction with others are key parts of that learning. Dewey recognises that every individual is not solitary, he exists within a society as ‘a social being, a citizen, growing and thinking in a vast complex of interactions and relationships’ (Dewey and Archambault, 1964). People create systems and meanings through those interpersonal interactions—which they can then use to understand everyday life. This is particularly important in the social world, as unlike the physical, natural world, many concepts are abstract and subject to individual interpretation. This drives my research motivation to understanding how people make sense of their world, and how that changes as a result of their lived experience.

Constructivists tend to believe that people are motivated by a desire to solve problems combined with a confidence to learn (Prawat and Floden, 1994). In this regard, I look beyond traditional constructivism, as I think individualism (Lukes, 2020) offers a better explanation. Much though we might not like it to be the case, people are self-interested, pursuing their own individual happiness and wellbeing, and it is this self-interest is what drives the pursuit of deeper knowledge and understanding about the world. In essence, everyone wants to improve their own life, and they need to acquire knowledge about how the world works so that they might be able to change it. This path of acquiring and sharing individual understandings about the world, which can then be used to effect change upon the world, is a key driver behind my research. Taken further, this can be seen as civil libertarianism (Gulite, 2014), which argues for the supremacy of individual rights and personal freedoms over imposition by authority. The human-centric movement [2.3.4] clearly subscribes to this philosophy, as do I, and this explains why my research moves from simply understanding the world in Part One, to functioning as an activist trying to change it in Part Two.

My established ontological stance, then, is that individuals construct concepts, and continually update them through sensory experience, action, and social interaction. They do this in order to maintain a pragmatic knowledge that they can practically exploit to effect change in society and in the world, in order to pursue their own happiness and self-interest.

Looking to epistemology: how can knowledge be acquired? Constructivism suggests that this is best done not through direct observation of the world and explicit testing of hypothesis, but though interacting and communicating with individuals so that we can interpret how they view reality; this is known as an interpretivist epistemology (Dudovskiy, 2012). This motivates my choices to favour qualitative (understanding perspectives and collecting spoken/narrative data) rather than quantitative (measuring behaviours and collecting numerical data) investigation. By understanding people’s views and mental models around data and digital living, I can look for commonalities and develop theories—powerful explanations that can be understood and benefitted from by ordinary people—to fill the knowledge gaps in existing research that I have identified [2.4]. Given my pragmatic focus on interpreting people’s constructed social realities in terms of practical usefulness (in individualist terms: individual benefit) to them, I will not be deeply analysing their words through language analysis techniques like discourse analysis. I will instead focus on the social, interpersonal level–understanding how people navigate the world of data and data-based relationships. I want to explore how they change their understandings as they pursue their goals in practice, and how they are affected by the systems, relationships and society they exist within. This pragmatic approach and search for commonalities motivates a mixed methods approach (UK Health Security Agency, 2020), combining qualitative or quantitative methods as appropriate to the particular sub-question being explored.

Thus, my epistemological stance is an interpretive one, that also recognises that in order to identify commonalities and shared meanings (Dudovskiy, 2012), the researcher must employ a mixture of qualitative and quantitative techniques.

3.2 Research Approach

Multiple changes, removing PAR and document the duality of research approach

3.2.1 Participatory Action Research & Experience-centred Design

Remove all reference to PAR

Moving from general research philosophy to specifics of this PhD, the purpose of the research is to formulate theories that can facilitate change - to map out a research and development agenda that might help the world to move from a data-centric [2.1] to a human-centric [2.3] operating paradigm. Learning about people’s understandings of their reality informs and enables an inductive research approach where patterns common to multiple people can be identified to form general understandings of what people want¹⁰ in relations with data and with those who hold it. Based on the premise of individual betterment laid out above, these desires serve as justification for the thesis to take a moral stance on personal data: That access to data held about you by others is a fundamental right, and that the to empower individuals to be able to understand and use their data for their own purposes is a fundamental good that will benefit individuals and society at large.

As a student of Digital Civics (Vlachokyriakos et al., 2016), I believe that research can surface the ways in which current service provisions fail to meet people’s needs, and how the world might better empower citizens if it were configured differently with services closer to what they desire. The role of the researcher is to understand the world and to figure out how to change it. It is an accepted view that research cannot be value-free, but in fact we can go further. The researcher can be an activist, seeking to correct an imbalance in the world through their research. As such, the design elements of this research can be considered as political. In this thesis, especially Part Two, I embrace adversarial design (DiSalvo, 2012). I view this as necessary to counterbalance the strong forces outlined in Chapter 2 that are acting against individual interests, and in order to pursue the moral imperative laid out above. By creating space to reveal and confront power relations and influence, we can identify new trajectories for action (DiSalvo, 2010). Therefore, the purpose of the participatory research in Part One is to inform myself as adversarial designer. Acquired insights from the experiences of research participants can help me to develop my own understanding and models.

When designing for people and trying to incorporate their views, there are traditionally two schools of thought: user-centred design (UCD) and participatory co-design (PD). In UCD, design is carried out by experts, who have undertaken user research to build up understandings of user needs (Norman and Draper, 1986). This approach places a high value on expertise, but it carries the risk that certain user needs may be overlooked, especially those that are less common (and therefore less likely be present in a designer’s concept of ‘the average user’). UCD is the most common approach used by technology companies today, not least because commercial motives must be incorporated into designs, and therefore design can never be fully democratised. UCD as implemented in modern software development practice does however recognise the importance of representing the user perspective in the design process, and uses processes such as focus groups, user experience testing, and user persona development to include their perspectives. However, such perspectives may ultimately be ignored or diluted in favour of expert designs or organisational motives, as observed in 2.4.

Recognition of this inherent problem–that users carry less influence than designers and that this imbalance must be tackled head on–lead to the ideas of co-creation (also known as co-design) and PD. PD is based upon the idea that those who will use or be affected by technology have a legitimate reason to be involved in its design (Kensing and Blomberg, 1998). PD is seen as an attempt to design in a more democratic fashion. PD proponents argue that it is not sufficient to study users and go away and design in isolation. Instead, the users and technologists work together in design workshops, with users bringing their lived experiences and perspectives and technologists bringing their expertise on technical and market possibilities and constraints (Bjerknes et al., 1987; Björgvinsson, Ehn and Hillgren, 2010; Smith, Bossen and Kanstrup, 2017). In such a collaboration, a collective, democratic design is created, taking into account all perspectives. In the 2000s, PD grew in popularity across public and private sector organisations, coincident with the growth of internet and social media into its Web 2.0 phase (Hosch, 2017) which resurfaced the potential to reframe digital technology as something to be harnessed for users’ own ends (Jenkins, 2006).

As design approaches, I see merit in both UCD and PD. The participant should play a role as an informant–one who can provide critical insights into their own perspective on a design space and help us understand how the world is to them–but also as a designer–one who can imagine how they would like the world to be. As we involve the participant, our role as researcher is to elicit the richest possible responses from the participant, by using questions to bring them to consider new possibilities and by giving them stimulating materials to trigger their thinking. The researcher also often needs to sensitise the participant to a design space, so that they may properly engage with the questions being posed. Conversely, the researcher cannot arrive at a model or theory unless he has developed empathy for the participant’s perspective. One of pragmatism’s founding philosophers, Peirce, put forward the pragmatic maxim, which states that the meaning of anything we experience in the world is understood through the conception of its practical effect, and that theories that are more successful at controlling and predicting our world can be considered closer to the truth (Campbell, 2011). Applying this philosophy to the challenge of design, I find merit in the different, less political, take on involving users as participants in design exhibited in McCarthy and Wright’s experience-centred design framework, which identifies processes to improve user empathy including interpreting, reflecting and appropriating (McCarthy and Wright, 2004).

Through this research I will at times be more participatory, to understand these aspects of user experience or to co-design solutions with participants. I will at other times act more like an expert designer, especially in Part Two. Taken to the extreme, the PD view is that designs made without the direct involvement of users are invalid, because they inherently no longer represent the desires of those people the designs claim to serve. I oppose this view, because I believe that new ideas will not always arise from participants themselves, especially for this research area–where a more expert-led experience-centred design approach is the most pragmatic way to proceed. By its nature, this research involves thinking about data, information, organisational relations and interaction at a level which the layperson is not accustomed or well-equipped to do. Therefore, while I strive to always include participant viewpoints, I give ultimate precedence in design to my own position of learning that I will acquire through the participatory research as well as peripheral design and development work [7.2]. In incorporating both approaches, I will also be a participant in my own research, incorporating my own experiences of living in a data-centric world (and my attempts to challenge it) into my learnings.

It is important to be clear about what constitutes good research in this context; if the outcome of the research is to be my own interpretations and theories, how will we know these are sound? First, it is important to say that this is not about measuring the effectiveness of proposed changes upon the world. There will be no deployment of systems to test the ideas I put forward. This is not because such an activity would not be worthwhile—it would—but simply because by its nature, to develop, build and deploy new data interaction paradigms that function in real life with real personal data at the sociotechnical level would be too large an endeavour for a single researcher (or even a single academic research group) to undertake. Therefore, what I seek in this thesis is not to change the world, but to articulate with the greatest possible clarity discrete theories on how the world should, and could, be changed. Good evidence for the proposed changes will be achieved by ensuring that findings, themes and discussion contributions are backed up by participant quotes or extant work and literature, and where an idea is suggested or agreed upon by many participants or where it resonates with the practical experiences of myself or of others, that can be seen as adding weight or validation to that idea. However, each person’s experience is unique and needs to be put into context; not every insight will be shared by many participants and individual unique insights remain important.

3.2.2 Action Research

Remove all reference to PAR Remove part A/partB split Document the hybrid research approach. Make clear the two parallel aspects of my work - participatory research, and grounded practical experimentation, cycles of action research feeding into each other. not PAR Update the action research diagram - Promote the dual participatory + industrial practice research - to show proper spiral back and forth. notes: Reworking of Structure of Part B (and indeed consider disbanding Part A/B split) to draw together unique approach of the PhD as a whole by making clearer and owning the methodological approach where there was feedback between Part A/B and tracking the feedback loops. This includes revising diagrams to show this level of interaction (e.g.the downward arrows and timeline ones) and not separating out one (Part A) as academic and the other (Part B) as not – they are linked and need to make more of this.

The mixed methods approach I adopt closely follows the discipline of participatory action research (PAR), which is an approach to research that encompasses both the involvement of participants’ perspectives while also retaining a role for the reflection and learning of the researcher themselves. PAR’s creator Kurt Lewin observed that ‘there is nothing so practical as a good theory’ (Lewin, 1951), highlighting the pragmatic nature of this approach. PAR combines self-experimentation, fact-finding, reasoning and learning, and makes sense of the world through collaborative efforts to transform the world rather than just observing and studying it (Chevalier and Buckles, 2008). Central to this is the idea that research and action must be done with, not on or for, people; participants are not subjects but co-researchers, evolving and addressing questions together (Reason and Bradbury, 2001). To embody the three ingredients of PAR (Chevalier and Buckles, 2019)—participation, action, and research—my research includes three types of activity:

1. participatory co-design activities - where I discuss and explore experiences, challenges and possible solutions with participants through conversations and design activities
2. self-experimentation activities - where I carry out experiments, ranging from thought exercises to practical tests of what is possible, to develop ideas and explore the problem space myself, and
3. embedded research activities - where I participate as an involved team member, in external organisations’ projects that are trying to change the world in this space, learning about the challenges faced and the viability of different approaches, on the basis of the grounded experience of myself and others (Cheetham et al., 2018).

In order to simplify the thesis, my embedded research activities (3) and self-experimentation (2) are not considered a core part of the academic research of the thesis. The Case Studies focus solely on participatory co-design. First, in Part One the thesis will work with participants to understand human needs around data, answering the research question through academic inquiry at this level in Chapter 6. Then, Part Two, beginning with Chapter 7, makes a conscious shift from PD to UCD, from investigatory research to adversarial design, using the Case Studies’ findings as requirements. At this point, those other activities will then take centre stage.

Action research also carries with it the idea that research is done in cycles: you learn something, carry out some action in the world based on your learning, learn from what happened, and repeat. This has become an established approach in HCI research (Hayes, 2011). The importance of collecting stakeholder feedback at regular intervals is also seen in the software industry though agile development (Fowler and Highsmith, 2001) which can be seen as a practical implementation of action research. In start-ups, terms like fail fast (Brown, 2015) and pivot (Ries, 2011) illustrate the idea that it is crucial to test ideas on real people then adapt quickly based on how that goes. To me, action research does not mean that you must test every single idea with an audience for it to be considered valid, but rather that user engagement is not a one-off, but a repeated component that affects the research path. Each new research activity will draw from past learnings and theories and acquired understandings so far, which are then developed through their exposure to real life in the process of participatory and embedded research activities.

Figure 3.1 shows the cycle of action research, as I apply it in this PhD. In each area of life or context I identify as a setting for a research activity, I first carry out initial background reading, experimentation or exploration to familiarise myself with the area, then design a research activity that helps to explore the research questions in that area. After carrying out the planned activity I analyse any data from that activity (or just reflect upon my experience) and then use these findings to update my overall understanding of how we might answer the research questions. I then go on to repeat this cycle, with each subsequent activity, but beginning anew with more developed theories or understandings. In the case of the peripheral research activities these run alongside the core PhD work, so analysis and learning happens throughout, resulting in a continually updating current understanding that forms the baseline for later research activities. In the next section, I will describe the two specific research objectives that are targeted through the Case Studies.

3.3 Research Objectives

Consider better contextualising w.r.t. hybrid dual approach

At the end of chapter 2, I introduced my research question, which is:

“What relationship do people want with their personal data?”

As identified in 2.1.5 and 2.2.5, there are two distinct research gaps to be explored. The Case Studies, as well as preliminary explorations that feed into them, will both be focused on exploring these two gaps, which I now formulate as two research questions RQ1 [3.3.1] and RQ2 [3.3.2]:

3.3.1 Research Question 1 (RQ1)

What is the human experience of personal data, and what do people want from their data?

Personal data, and its collection and use by commercial and civic organisations, is an established and inevitable part of modern life [2.1], yet the concept of data is abstract and poorly understood . The first objective therefore is to establish a solid understanding of what mental models people have constructed about data. What makes data meaningful to people? Given HDI’s belief that everyone needs a relationship with their data, what relationship do people currently have with their data? What is data to people? How does that relationship affect them, and what are their unmet desires for improving their relationship to their personal data? What aspects of data cause positive emotions? What problems do people experience with their data? What people want from their data?

I will use a participatory approach to address these questions: gathering individual perspectives on data, and looking for patterns or trends in those perspectives. The first challenge here will be to find ways to sensitise participants to be able to conduct an informed and productive conversation about the topic of data, which to the layperson may seem a dry, boring topic. This challenge will be addressed by leading participants into the subject of data using meaningful representations of data as stimulus for conversation, or starting with the individual’s own life experience to discover the data in their life, which they are more likely to have opinions and emotions about, rather than talking about the subject in the abstract.

3.3.2 Research Question 2 (RQ2)

What role does data play in people’s service relationships, and how could relationships involving data be improved?

In section 2.2 and 2.3, I established that as of yet, designers of PIM and personal data interfaces have not yet risen to the sociotechnical challenge of looking at the reality of personal data today: it is scattered, inaccessible and largely un-useable¹⁰. There is no way for people to view their data holistically, as lamented by Ms. Heap in the quote atop Chapter 1. Nor are there any tools to help people manage the many relationships that individuals have with companies, employers, councils, governments and other organisations that rely heavily upon the collection and processing of their personal data. Almost every civic or commercial service we use today handles data about us. We know that the world is data-centric, and that data controllers use data as an asset to inform their decision making, creating a serious imbalance of power (Hoffman, 2010, 2011, 2013, 2014a, 2014b). But what is like to conduct a relationship with an organisation that holds your data? What emotions do people experience? How does it affect their daily life, and what sort of problems do people face as a result of this data-centricity? If your data is used in ways you do not understand or consent to, how does this affect your outlook on the world?

This is the second strand of research I explore: to gain an understanding of the data world beyond the individual [2.2.5], so that we can design not just better individual relationships to one’s data, but improve people’s relationships with organisations that hold and use data. For the purposes of this study, I only pay attention to service relationships (the primary place where data is held), not social or interpersonal relationships.

To tackle RQ2, participatory research approaches are again appropriate, as our questions relate to the individual mental constructs that people have about their wider digital lives and relationships. But there is another aspect here, and that is that a relationship involves two parties. Consistent with Dewey’s belief in the importance of interaction in creating meaning, the structuralist philosopher Michel Foucault said that ‘meaning comes from discourse’ (Adams, 2017), in other words people do not construct their reality in isolation, but in fact it is shaped by the social constructs and systems they operate within. Deweyan pragmatism also takes the view that research must seek solutions to real world problems that are generalisable to use in society at large (Dewey and Archambault, 1964; Friedman, 2006). This implies that any such solutions arising from my research must work for all parties. For both these reasons, I aim to conduct participatory research to understand both perspectives: that of the data controller and that of the data subject, and where possible I will engage both parties together in discourse so that the two parties’ worldviews can be brought together to design solutions that could work in practice for all involved.

This second research objective will be tackled in tandem with the first, so that in each research setting we can examine the situation at two levels–to look introspectively at the individual’s own relationship in service of RQ1 [3.3.1], but also to take a step back and look at the wider social context the individual is operating within, so that we might be better placed to answer RQ2 [3.3.2].

renumber section

3.4 Methodologies Employed in Case Studies

good

Having introduced my research approach and explained the research contexts, Case Studies and activities, I will now explain the methods were used across the Studies and why they were chosen. Loosely the methods used can be grouped into five stages, though not every activity involved all stages:

1. Sensitisation of Researcher and Participants
2. Discussion and Exploration with Stimuli
3. Participatory Co-Design of Possible Solutions
4. Practical Data Experiments, Interface Design and Prototyping
5. Data Analysis and Thematic Synthesis

I will now explain each of these stages, with examples from the different Studies, as well as providing information about recruitment and ethics at the end of this section.

3.4.1 Sensitisation of Researcher and Participants

good

As I described in 3.2.2, an important first step before each research activity is to sensitise myself as researcher to the context, which means to become familiar with relevant issues, systems and practices and increase my empathy for the participants. In the pilot study [3.4.1], this entailed a grey literature review to identify the different types of civic data that councils store, and conversations with colleagues and partner organisations within the SILVER project to deepen my understanding of Early Help. The pilot study [1.3.1] combined with this to serve as researcher sensitisation for Case Study One, as participating families had had some contact with the care system. This increased my empathy for, and understanding of, supported family perspectives. Working with support workers through the SILVER project also provided empathy for the data needs of staff within the care service. In Case Study Two, my self-experiments with GDPR as well as researching privacy policies and GDPR rights provided me similar sensitisation before engaging participants.

Participants need to be sensitised too; when planning participatory research activities such as interviews or workshops, it is important to begin the session with an activity that will acclimatise participants both to the specific area of discussion, but also to the problem-solving mindset required for constructive conversation. This goes beyond ice-breaking, and into thinking about what the participants bring and lack at the start of the engagement. For example, in the pilot study, I felt that data would be a hard topic for families to engage with, so I designed the Family Facts activity [Figure 3.2]. This required family members to consider simple facts about their lives (some provided, some created by the family members) and discuss whether or not such a fact would be considered data, and additionally whether such a fact should be in the family’s control or that of the authorities. This served a double purpose of teaching families that data is simply ‘stored information about you’, while also getting them used to thinking critically about data ownership. The technique is discussed further in (Bowyer et al., 2018).

For Case Study Two, I wanted participants to think about the data involved in their everyday lives, especially that stored by commercial service providers. So, I put up a series of posters in the common room of my research lab which showed logos of companies that might store data, types of data that might be stored, information about GDPR rights, and possible uses that an individual might have for data they obtain from a GDPR request. Some of these posters are shown in Figure 3.3. These posters served both as a recruitment tool for the project and a discussion prompt for participant onboarding.

Sensitisation activities can also serve an additional purpose of bringing disparate participants to be ‘on the same page’. This is known in participatory research as co-experience (Battarbee and Koskinen, 2005). An example of this is the sentence ranking exercise used at the start of all workshops in Case Study Two [Figure 3.4]. Here, a series of sentences were prepared containing opinions about civic data that had been observed from staff and families in earlier research. Participants were asked to rank these according to agreement and importance. This allowed me to validate whether previous findings still held with these participants, but also acclimatised the participants to considering and discussing the civic data context and the problems experienced by families and staff. Since the sentences included both staff and family viewpoints, and the activity was carried out in all workshops regardless of whether staff, families or both were present, it served to establish a common set of ‘requirements’ that would be in participants’ minds as they began the subsequent co-design activity within each workshop.

3.4.2 Discussion and Exploration with Stimuli

add a few words/refs to big up the data cards a bit more (maybe a contribution in the intro too? ). notes: The value of cards based tools for ideation and situating legal/ethical discussions in context, so was pleased to see the family civic data cards being used as a tool to facilitate reflection and discussion (cf. The Moral IT Deck link). You could have made more of this development too as a novel approach if you wanted to, as a toolkit for engaging empirically around policy/law (as these tools are lacking, despite calls from legal frameworks to support design of more legally informed systems e.g., GPDPR data protection by design and default).

As discussed in 3.2.1, my research seeks to uncover individual perspectives and worldviews. The primary method that I used in both Case Studies was traditional qualitative interviewing - talking to people about the topic being explored. In Case Study Two, this was largely done on 1-on-1 basis (largely because of the sensitivity of dealing with one’s own personal data, and because it allowed me as researcher to get closer to the participant’s individual experience). In Case Study One, group discussions and activities were mainly used, which brought the advantage of being able to prime a discussion between participants and then sit back into more of an observational role, which proved particularly insightful when observing intergenerational conversations between family members in the pilot study [3.4.1], and in Case Study One it allowed me to observe the negotiation of a ‘middle ground’ between support workers and supported families. In some cases, such as the home visits in the pilot study and local authority visits for SILVER, I was able to conduct interviews-in-place (Pink et al., 2013) in participants’ own environments, which allowed for additional ethnographic observations to be made as ‘life happens around’ (Mannay and Morgan, 2015) the participants, as discussed in (Bowyer et al., 2018).

I wanted to go beyond ‘just talking’ to achieve a deeper and more detail-oriented conversation. In all of my interviews and group engagements, I also ensured that suitable stimuli were created to seed and progress the discussion. Given the abstract nature of the topic of data, it does not always carry a clear meaning in people’s everyday lives, so I needed to find a way to make the topic more vivid and real. Having sensitised myself to civic data as mentioned in the previous section, I constructed a taxonomy and lexicon for Family Civic Data, and created Family Civic Data Cards [Figure 3.5] for use in activities and discussions. These serve as boundary objects (Star, 1989, 2010; Bowker et al., 2015) - representational artefacts that are understandable by people who come from different perspectives, providing a common vocabulary for discussion (as well as serving to enable co-experience, detailed above). Each card represents a different category of data, including a summary and meaningful examples to make them be easy to digest, yet still containing sufficient detail to stimulate thinking. The cards were designed to be bright, child-friendly and appealing to engage with. The tangibility of these artefacts was important too. They became things to think with (Papert, 1980; Brandt and Messeter, 2004) in discussions and in activities. Researchers have had success with the use of tangible objects to embody discussion concepts in order to stimulate and structure discussion, for example Coughlan’s use of a dolls’ house to explore attitudes to home energy use (Coughlan, Leder Mackley, et al., 2013) or more recently Xie’s Data City which used AR-enhanced cardboard models to represent data-processing functions (Xie, Ho and Wang, 2021). These approaches have their roots in Dourish’s concept of embodied interaction (Dourish, 2001). The cards were used throughout the Civic Data research in both sensitisation and card sorting (Spencer and Warfel, 2004) tasks, for example asking participants to position the cards on a pinboard according to perceptions about risk and ownership [Figure 3.7], or sorting them into trays according to relative personal importance. The cards proved very effective at enabling a personal and detail-oriented discussion. Participants voluntarily opened up about sensitive topics (e.g. domestic violence or criminal records) raised by the cards because of their detached-but-relatable nature (Bowyer et al., 2018). In Case Study Two, discussions around data did not use data cards, but the importance of meaningful examples to make the topic relatable persisted, in this case being demonstrated through posters [3.5.1] and in particular a categorisation of example data by category [Figure 3.6], similar to the examples on Case Study One’s data cards. I went on to develop data cards for use in participatory research at BBC R&D [Figure 9.16; Figure 9.17].

The sketching dialogue technique [Hwang (2021); Figure 5.2] used in the digital life context can also been as another stimulus technique; by putting both participant and researcher’s focus upon the page, rather than on each other, it can feel less invasive, more collaborative and makes it easier to focus on details. The ideal stimulus for discussion about data is to view the actual data itself. Due to the sensitivity of personal data, this is more easily done 1-on-1 than in a group. Exploring data together with participants to elicit opinions and insights is a well-established technique (Coughlan, Brown, et al., 2013; Chung et al., 2016; Puussaar, Clear and Wright, 2017). This is the technique used within Case Study Two, asking participants about the data they retrieved from GDPR requests, using a spreadsheet-based approach 3.4.2. This allowed the Zoom-based interviews to retain a ‘gathered around the table looking at things together’ ambiance despite the remoteness necessitated by COVID-19 restrictions.

3.4.3 Participatory Co-Design of Possible Solutions

good

Participatory Design (PD) [3.2.1] becomes particularly important when exploring solutions and ideals to identified problems. It involves bringing participants into a new mental space where they can imagine the realm of the possible, rather than just their current lived experience. PD was an important part of Case Study One research with all participants. In the early stages of a PD activity, it is important that participants are able to generate a wide range of ideas, even fantastical ones, without constraints, self-censoring or judgements. This is known as the discovery phase in the UK Design Council’s double diamond framework. (Design Council UK, 2004). Golembewski’s ideation decks technique (Golembewski and Selby, 2010) was chosen for this purpose [Figure 3.8], as it allows participants to both select ‘ingredients’ of a design based on their own experience but also to combine them in a variety of different ways to generate novel ideas, guiding them into a previously unconsidered solution space.

After generating a wide range of ideas using the ideation decks, participants were then invited to pick just one or two ideas to develop into posters, each with three ‘features’ highlighted. An example is shown in [Figure 3.9]. This activity corresponds to the define phase of the double diamond, where participants narrow down options.

For the final workshop of Case Study One, where both parents and staff were brought together to explore possibilities of shared data interaction within the support relationship, I used a storyboarding activity. Drawing from the world of film production, storyboarding is a well-established technique in participatory design (Spinuzzi, 2005; Moraveji et al., 2007). Usually it involves the participants drawing out a series of sketches in the form of a comic strip ‘telling the story’ of an interaction, encounter or activity. However, given the need [1.1.2; 3.2.1] to focus on interpersonal relations and process rather than the visual aspects of storytelling or interface design, and drawing upon earlier successes with data cards, I used a card-based approach to storyboarding, where participants selected actions from a palette of action cards representing different possible human or data interaction possibilities and annotated these with specific details. These cards are shown in Figure 3.10 and described in more detail in ARI4.3.

3.4.4 Data Analysis

good

In order to find common viewpoints and extract insights from the many participatory activities I conducted in Case Study One and Two, I needed to analyse the qualitative data. The general approach taken was to audio record (and, in some cases, video record) all interviews and workshops, and to produce a written transcript of the words spoken. Digital photos were taken to capture card arrangements, rankings and other transitory choices, as well as designs, life sketches and other participant creations. While it is possible to analyse participant designs in more detail, I chose to give them the sole purpose of adding contextual understanding to conversation transcripts and did not examine them further. Field notes were captured during or soon after each engagement. Then a process of thematic analysis was undertaken. This involved examining the text of the transcripts (with reference to all relevant digital artefacts to add context), and identifying the underlying ideas, themes and opinions of the participants. Thematic coding is a well-established technique in qualitative research (Braun and Clarke, 2006). I selected the Quirkos software for this purpose, as shown in Figure 3.11, due to it having a more visual organisation and simpler approach than the more commonly used nVivo. After initial coding of transcripts, a process of reductive data display cycles (Huberman and Miles, 2002) was used to group codes into themes which became the key findings of Chapters 4 and 5.

While the participant data in Case Study One and Two was largely free-flowing and very loosely-structured conversation, the structure of some activities allowed some data to be captured numerically, notably the sentence rankings and data card placements in Case Study One and the trust/power ratings and GDPR spreadsheets produced in Case Study Two. These datapoints were captured into Excel spreadsheets, and where appropriate analysed using formulae to produce weighted mean averages and standard deviations to help contextualise the findings. An example is shown (as evidence rather than explanation) in Figure 3.12. Due to the qualitative focus of the research, participant numbers were too low to seek statistically significant findings, so all quantitative findings are not intended to be representative of any population at large.

3.4.5 Recruitment

good

Tables 3.1 and 3.2 summarise the participants involved in this research. In Case Study One, recruitment was initially attempted using posters placed in local libraries as shown in Figure 3.13 below. When this approach was unsuccessful, participants were successfully recruited with the assistance of a local community centre, which allowed me to visit a community social meeting and talk to residents about my study. This community was located in a low-income area that was known to include a number of supported families; this was chosen with a view to reaching a similar population as SILVER.

For the main engagement of Case Study One, I was able to work with two local authorities, Newcastle City Council and North Tyneside Council, who were partners on the SILVER project, and provided suitable participants who were actively involved in their Early Help programmes. In the pilot study and in the first families workshop of the main study (stage 1A), activities were designed to include children as active participants in the research. It was felt they would bring valuable contributions to the somewhat abstract creative co-design work and this also allowed observation of intra-family conversations. The final combined workshop with staff (stage 2) was designed to only include adult participants. This is because the focus on processes and on the care relationship itself was thought to be too boring and potentially sensitive for the children to participate.

In Case Study Two, no special population was needed, as the issues of living in a data-centric world affect everyone. Therefore, a convenience sample (largely 20-to-40-year-old postgraduate students from Newcastle University) was used. Care was taken to find an even split of male and female participants, but other than that no selection criteria were applied. Given their proximity to the Digital Civics programme, the participants for this study were thought likely to have a larger awareness of societal issues around personal data use and greater familiarity with participatory co-design than the layperson. This was considered an advantage as they would require less sensitisation.

In all cases² for both case studies, participants were compensated for their time with vouchers—either online/offline shopping vouchers or vouchers for a family day out.

3.4.6 Ethics

good

All research activities referenced in this thesis were planned in advance, with interview schedules, information sheets, debriefing sheets, participant consent forms and ethics forms being completed and submitted to Newcastle University’s SAgE faculty ethics board, which approved all the studies before they commenced. Ethics approvals are included in Appendix B. Most of the engagements were routine interviews and therefore did not require any special measures for safety or ethical reasons. It was made clear to all participants that they were free to withdraw from my research at any time without giving a reason. The following special measures were included in plans in order to satisfy ethical considerations:

1. Visiting private homes: In order to protect myself and other researchers from any physical risks or any accusations of impropriety, all home visits took place with two researchers present, and contact was made with a colleague before and immediately after the interviews to confirm everything was ok.

2. Working with children: Activities were designed to be child-friendly (not just safe, but engaging). The families workshop took place at in a function room within a park with a nearby cafe and playgrounds for children, and catering was provided. Within the room, an activity area was provided for smaller children who were not directly participating to play while their parents and older siblings engaged. There was always more than one researcher present and the research team was never alone with children.

3. Protecting personal data privacy: In Case Study Two, particular care was taken to design ways for researchers to talk to people about their personal data without violating participants’ right to privacy. The research was positioned that the data retrieved from companies was participants’ own data, that would never be directly collected or handled by the research team. It was made clear that the researchers were only interested in what was said, not personal data itself. Initially, a privacy monitor was developed which could only be seen with viewing glasses that were in the participant’s control [See ARI3.1]. This would allow a researcher to sit next to a participant who was viewing his/her personal data, without the researcher being able to see it. Additional measures to protect users’ data included clear instructions on how to keep data safe before, during and after the study. A complaints procedure was also written at the request of the Ethics board.

4. Adapting to COVID-19: As COVID-19 changed working and living conditions in early 2020, Case Study Two was adapted to no longer rely on face-to-face engagement. The in-person privacy monitor approach mentioned above was abandoned and replaced with an online Zoom-based approach. In this model, participants would share parts of their data using screen sharing instead, and could move windows off screen to protect their privacy. The full study plan for Case Study Two was rewritten for online-based participation and was re-approved by the Ethics Board.

3.5 Summation: Towards an Understanding of Better Data Relations

update 3.6 so it doesn’t mention part A/B and ties in with previous rewrite to round up section I and set up section II. conclude with what will happen in next two sections

As established earlier [1.4; 3.2.1], the primary focus of the participatory co-design research in this PhD is to arrive at an understanding of what people want from data and in relationships involving data. Figure 3.14 shows three parallel research objectives as downward arrows, considered as three trajectories of evolving understanding. The first two of these correspond to RQ1 [3.3.1] (which can be seen as understanding personal data), and RQ2 [3.3.2] (which can be seen as understanding data in relationships). The positioning of activities as boxes along these arrows throughout the period of this PhD, indicates that that activity contributes to that area of understanding, and collectively this shows how all activities contribute towards delivering that understanding - in Chapter 6, as an answer to RQ1 [3.3.1] and RQ2 [3.3.2].

Figure 3.14 also shows how the peripheral adversarial design and industrial research & development activities run alongside the Case Studies, developing an understanding of how human relationships with data can be improved in practice (the third downward arrow). These peripheral activities [detailed in 7.2] form the basis of Part Two of the thesis. Given they ran in parallel, they also informed the participatory research work throughout the PhD, and vice-versa, in line with the action research approach shown in Figure 3.1.

Using the methodologies in this chapter, Case Studies One and Two will answer RQ1 [3.3.1] and RQ2 [3.3.2], with the findings across the two studies being synthesised in Chapter 6 to conclude Part One, before Part Two moves forward from investigatory research into adversarial design and activism techniques (detailed in that chapter), to chart opportunities for bringing the identified individual desires of the main thesis research into reality [Chapter 9].

Bibliography

good